A cell signaling model as a trainable neural nanonetwork

All cells have to adapt to changing chemical environments. The signaling system reacts to external molecular ‘inputs’ arriving at the receptors by activating cellular responses via transcription factors generating proper proteins as ‘outputs’. The signal transduction network connecting inputs and outputs acts as a molecular computer mimicking a neural network, a ‘chemical brain’ of the cell. The dynamics of concentrations of various signal proteins in the cell are described by continuous kinetic models proposed recently. In this paper we introduce a special neural network model based on the ordinary differential equations of the kinetic processes. We show that supervised learning can be implemented using the delta rule for updating the weights of the molecular neurons. We demonstrate the concept by realizing some of the basic logic gates in the model.